Paths

AWS Big Data

Processing big data jobs is a common use of cloud resources mainly because of the sheer computing power needed. AWS has created several services that enable you to use big data effectively for your projects. This path will teach you the basics of big data on AWS.
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Big Data on AWS: The Big Picture

Description

This course will teach you big data fundamentals in the context of Amazon Web Services (AWS), the leading cloud computing platform. In this course, Big Data on AWS: The Big Picture, you will learn foundational knowledge of big data concepts and the major big data services on AWS. First, you will learn all about core big data concepts, like data lakes, NoSQL and MapReduce. Next, you will discover the array of big data services available on AWS and how they tie together. After that, you'll learn the details of each service and see many of them demoed. When you’re finished with this course, you will have the skills and knowledge of big data on AWS needed to understand which combination of services is best suited to your organization's skill sets and which best meet your organization's needs.

Table of contents

Course Overview

Introduction: Big Data Concepts

AWS Big Data Services

Batch Analytics with Elastic MapReduce (EMR)

AI and Streaming Data Processing with EMR

Data Warehousing with Amazon Redshift

(Big) Data Integration and Pipelines

Visualizing Your Big Data with QuickSight

Strategy

Serverless Analytics on AWS

Description

How to architect and build big data analytics in the AWS cloud in the day of AI and ML has been transformed by both AWS Glue and Amazon Athena. In this course, Serverless Analytics on AWS, you'll gain the ability to have one centralized data source for all your globally scattered data silos regardless if the data is structured, unstructured, or semi-structured so you can perform multiple types of advanced analytics on the data by multiple people simultaneously without affecting the underlying data store wherever in the world each data set is located, keeping the data in sync with any changes to the source data. First, you'll learn how to use AWS Glue Crawlers, AWS Glue Data Catalog, and AWS Glue Jobs to dramatically reduce data preparation time, doing ETL “on the fly”. Next, you’ll discover how to immediately analyze your data without regard to data format, giving actionable insights within seconds. Finally, you’ll explore how to use AWS best practices to keep up by having AI and ML analytics incorporated into your analytics workflows, future-proofing your data via immutable logs. When you’re finished with this course, you'll have the skills and knowledge of using state of the art serverless technologies to provide a myriad of insight types whenever you need them.

Table of contents

Course Overview

Download and Install Course Prerequisites

The State of Analytics in the AWS Cloud

Infrastructure and Data Setup via Amazon CloudFormation

The Power of AWS Glue

Creating AWS Glue Resources and Populating the AWS Glue Data Catalog

The Power of Amazon Athena

How to AI and ML Your Apps and Business Processes

Intermediate

In this section you will learn about the different databases and storage options that you can implement on AWS that are suited to big data jobs.

Implementing Amazon S3 Storage on AWS

Description

AWS S3 is one of the most fundamental services offered by Amazon. S3 is also used by several other AWS services as well as Amazon's own websites. The securing, auditing, versioning, automating, and optimizing cost for S3 can be a challenge for engineers and architects who are new to AWS. In this course, Implementing Amazon S3 Storage on AWS, you will gain the ability to get the most out of your Amazon S3 service. First, you will learn how to create buckets, upload objects to the storage class matching your need and budget, and retrieve them. Next, you will discover how to apply the recommended security practices to your S3 buckets and audit access to them. Finally, you will explore how to work with multiple object versions, archive cold data in S3 Glacier, and configure life-cycle rules to automatically save big on your S3 costs. When you are finished with this course, you will have the skills and knowledge of Amazon S3 needed to use it as your main cloud-based storage option.

Table of contents

Course Overview

Creating S3 Buckets

Securing Your Data

Managing S3 Buckets

Transferring and Migrating Data

Data Life-cycles

AWS DynamoDB Deep Dive

Description

With recent advancements in modern technologies, such as the sharp growth of the IoT sector, we need databases that can handle loads that are magnitudes higher than before. AWS DynamoDB is a NoSQL database that addresses these new challenges. It is easy to operate and has a myriad of powerful features. Unlike other databases that require complicated installation and support, DynamoDB allows you to bootstrap a fully-fledged database that can operate on a high scale within minutes. In this course, AWS DynamoDB Deep Dive, you will learn how to develop applications that fully utilize the power of DynamoDB. You will explore how to process a stream of updates to DynamoDB tables in real time, how DynamoDB works under the hood, how to use DynamoDB transactions, how other AWS services integrate with DynamoDB, and how you can use them to get the most out of it. By the end of this course you will have a deeper understanding of DynamoDB, one of the core services which should be studied by anyone who is serious about using AWS.

Table of contents

Course Overview

Introduction to DynamoDB

Getting Started with DynamoDB

DynamoDB API

Introduction to the High-level Interface

Queries with the High-level Interface

DynamoDB Streams

DynamoDB Transactions

DynamoDB Best Practices

Data Analytics with DynamoDB

Building Your First Amazon Redshift Data Warehouse

Description

Amazon Redshift brings the power of scale-out architecture to the world of traditional data warehousing. In Building Your First Amazon Redshift Data Warehouse, you will explore this low cost, cloud based storage that can be scaled up or down to meet your true size and performance needs. First, you will learn to stand up and configure a sample data warehouse. Next, you will explore the internal workings and architecture of Redshift and what makes it so fast. Finally, you will get hands on experience connecting, querying, and building BI and data viz products as well as learn how to secure, maintain, and administer your new platform. By the end of this course, you will be able to scale from gigabytes to petabytes on this high performance column-oriented SQL engine.

Table of contents

Course Overview

Answering the Question: "Why Amazon Redshift?"

Populating Redshift

Connecting, Querying, and Consuming Data

Securing Your Data Warehouse

Exploring Advanced System Topics

Advanced

In this section you will learn about the services that aid in processing your streams of big data on AWS.

Developing Stream Processing Applications with AWS Kinesis

Description

365体育足球The landscape of the Big Data field is changing. Previously, you could get away with processing incoming data for hours or even days. Now you need to do it in minutes or even seconds. These challenges require new solutions, new architectures, and new tools.

In Developing Stream Processing Applications with AWS Kinesis, you will learn the ins and outs of AWS Kinesis. First, you will learn how it works, how to scale it up and down, and how to write applications with it. Next, you will explore how to use a variety of tools to work with it such as Kinesis Client Library, Kinesis Connector Library, Apache Flink, and AWS Lambda. Finally, you will discover how to use more high-level Kinesis products such as Kinesis Firehose and how to write streaming applications using SQL queries with Kinesis Analytics.

365体育足球When you are finished with this course, you will have an in-depth knowledge of AWS Kinesis that will help you to build your streaming applications.

Table of contents

Course Overview

Kinesis Fundamentals

Developing Applications Using Kinesis Client Library

Implementing Advanced Kinesis Consumers

Funneling Data with Kinesis Firehose

Implementing Stream Analysis Applications Using Streaming SQL

Building an Elasticsearch Cluster with Amazon Elasticsearch Service on AWS

Description

365体育足球AWS Elasticsearch is a lightning-fast real-time analytics and search engine. In this course, Building an Elasticsearch Cluster with Amazon Elasticsearch Service on AWS, you'll learn foundational knowledge of AWS Elasticsearch. First, you'll gain the ability to upload, index, and search data using the service in AWS. Next, you'll discover how Kibana can be used to visualize large datasets in Elasticsearch. Finally, you'll learn how to create a fully functional search application powered by Elasticsearch and related AWS services. When you're finished with this course, you'll have the skills and knowledge of Elasticsearch on AWS needed to create custom search engines and real-time analytics dashboards.

GitHub link with all code used in demo's - http://github.com/JordanYankovich/Pluralsight-AWS-Elasticsearch-Product-Search

Table of contents

Course Overview

Contextualizing Elasticsearch

Creating a Simple Search

Creating a Robust Search Engine

Handling and Analyzing Data with AWS Elastic MapReduce

Description

A lot of people hear about big data analyzation, but how can you use it for your use cases? In this course, Handling and Analyzing Data with AWS Elastic MapReduce, you’ll learn foundational knowledge and gain the ability to use AWS Elastic MapReduce to perform data analyzation. First, you’ll explore configuring AWS EMR and Hadoop. Next, you’ll discover how to process, move, and query data using big data frameworks. Finally, you’ll learn how to stream and analyze data using Apache products and MLlib. When you’re finished with this course, you’ll have the skills and knowledge of using AWS EMR needed to handle and analyze your own big data datasets.

Table of contents

Course Overview

Configuring Elastic MapReduce in a Pipeline

Processing, Moving, and Querying Data

Streaming and Analyzing Data with Apache Products

Adding Machine Learning to the Pipeline

AWS Big Data in Production

Description

As the world of business continues to operate at a normal pace, the amount of data that is generated grows almost exponentially. Handling this increase in data requires both intelligent applications and smart tooling. In this course, AWS Big Data in Production, you will learn how to strategically implement big data on AWS in production environments. First, you will learn how to automate infrastructure provisioning with CloudFormation all the while controlling costs. Next, you will discover how to secure customer data through IAM and encryption at rest with S3 and EBS. Finally, you will explore how to visualize data using QuickSight. When you're finished with this course, you will have the skills and knowledge of big data practices needed to enrich your current big data systems.

Table of contents

Course Overview

Automating Governance with CloudFormation

Securing Data with IAM and Encryption at Rest

Monitoring Availability with CloudWatch

Visualizing Data with QuickSight

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